Generalized forgetting functions for on‐line least‐squares identification of time‐varying systems

The problem of on‐line identification of a parametric model for continuous‐time, time‐varying systems is considered via the minimization of a least‐squares criterion with a forgetting function. The proposed forgetting function depends on two time‐varying parameters which play crucial roles in the stability analysis of the method. The analysis leads to the consideration of a Lyapunov function for the identification algorithm that incorporates both prediction error and parameter convergence measures. A theorem is proved showing finite time convergence of the Lyapunov function to a neighbourhood of zero, the size of which depends on the evolution of the time‐varying error terms in the parametric model representation. Copyright © 2001 John Wiley & Sons, Ltd.